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Data Modeling Workshop
| Duration: 2 days |
| Participants: Project leaders, systems analysts, data analysts, database administrators, data administrators, end users, and others involved in modeling data. |
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Objectives:
Upon successful completion of this course you will be able to:
- Apply data modeling principles to the business requirements of your organization. - Produce stable, accurate, relational data models. - Understand and implement the steps involved in building a data model including Entity-Relationship modeling and normalization. - Describe dimensional techniques used for Data Warehouses (Star and Snowflake Schemas). |
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Overview:
- This course is designed to teach you to use current data modeling principles to organize your data for efficient reporting and maintenance. In the case study, you develop a high-level and detail-level data model using a step-by-step process to refine and normalize the model. You will be able to describe different data stores, from flat files, hierarchial and network to relational and object-oriented databases, data warehouses, and star and snowflake stores. Related topics, such as denormalization, dimensional modeling, Data Warehouses, Star and Snowflake schemas, and UML for data modeling are presented and contrasted with E-R Modeling. - Our DB2 UDB Database Administration course is designed to follow this course and the model developed in this first course is then used to create DB2 tables and indexes in the DB2 UDB DBA course. |
| Prerequisites: None. |
| Format: Lecture, case study, and classroom exercises. In the case study, you will step through the process of developing a high-level and detail-level data model and refining and normalizing the model. |
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Topic Outline:
- Introduction to Logical Database Design Data modeling concepts Importance of data modeling Benefits of data modeling Data independence Data-driven systems development Data modeling and the system development life cycle Data modeling approaches (top-down / bottom-up) Iteration in data modeling High-level to detail-level data modeling - Entity Relationship (E-R) Data Modeling Objectives of E-R data modeling - Discovering Entities Entity vs entity occurrence Entity classifications Naming entities Entity definitions - Discovering Relationships Relationship vs relationship occurrence Recursive relationships Parallel relationships Relationship cardinality Relationship optionality Naming relationships Relationship definitions - Cardinality - Generalization/Abstraction Disjoint ISA Non-disjoint ISA Subsets/Supersets - Attributes Discovering attributes Attribute vs attribute occurrence Basic attributes Derived attributes Designed attributes Composite attributes Attribute domain Naming attributes Attribute naming problems Attribute definitions Assigning identifiers Identifiers as candidate key - Data Model Refinement Refining relationships Relationship redundancy Resolving many-to-many relationships Bill-of-materials structure Refining entities Entity subtypes Entity life cycle analysis - Conceptual Data Modeling One-to-One, One-to-Many, Many-to-Many relationships Association entity Keys: Primary keys, Foreign keys Integrity - Normalization 1st normal form 2nd normal form 3rd normal form Higher normal forms Over normalization Denormalization Practical guidelines for normalization - Synthesizing User Views Local views of data Independent data model development Elimination of redundancies Resolution of conflicts Practical guidelines for synthesis - Data Model Confirmation Data model completeness Data model correctness Data model coverage Data model consistency - Transition from Data Model to Database Quantifying the data model Access path analysis Denormalization Transforming the data model into the DBMS |
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